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Monitoring Patients’ Signs Wirelessly

Platform : EMBEDDED

IEEE Projects Years : 2012 - 13

Monitoring Patients’ Signs Wirelessly

 

ABSTRACT

 

                                                          Our system applied DTW method to recognize human activities of daily living. We used two feature sets to the classifier such as database and test data. We kept signals to the reference databases from two healthy subjects, male is 24 years old and female is 23 years old. Train data were recorded from different human activities.. By the way, test data were the test signals inputs. These test signals were recorded every two seconds (sampling time 70ms). Each test signal was computed in DTW method with ten activities reference database as follow DTW method computed two signals per time: test signal and reference database signal. So, DTW was used for ten times per each test signal. In this method, we got ten optimal warp paths as above-mentioned. After that, we found the minimal value from these ten optimal warp paths.

 

                                                          The purpose of the project is to that tracks human movements and behaviors for rehabilitative purposes. We applied Dynamic Time Warping (DTW) to recognize human activitiesof daily living. Different movements are considered and were kept to reference databases signals. This project consists of two parts: transmitter and receiver. A transmitter part is the device mounted at the user’s waist within a pager case measuring 90x40x20 mm. The whole device weighs approximately50 g including batteries. A sensor used in this device is a 3-axial accelerometer. The signals from the accelerometer are transmitted wirelessly to a personal computer in receiver part using Zigbee 2.4GHz. A personal computer only requires visual basic program to recognize our system. DTW is used to match the signals from different behaviors in online with the databases. DTW will find a minimal path between two time series: the test signal and the reference database signal. This minimal value can classify a kind of activity of that test signal. The experiment shows 91 percent accuracy in recognizing these behaviors.

 

ADVANTAGES:

 

1.  elder/disable persons move remotely without anybody presence

 

2. Autonomously transfer between different locations



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